Self-Attention based Deep Hash Learning Method for Efficient Image Retrieval
Authors: 
Fei Yin, Xizhan Gao, Sijie Niu, Hui Zhao, and Guang FengAuthors Info & Claims
Fei Yin, Xizhan Gao, Sijie Niu, Hui Zhao, and Guang FengAuthors Info & ClaimsSeptember 2023
Pages 403 - 408
Abstract
Hash method with efficient retrieval efficiency and lower storage space, in the most recent has been widely used in image retrieval task. For complex image retrieval task, the manually extracted features often has limitations, and the deep convolution neural network can extract better features. However, traditional convolution neural networks usually focus on local features, while ignoring the ability to learn global features. In addition, traditional hash methods require constructing sample pairs to learn distance metrics, resulting in high computational costs. To address the above problem, in this paper a new self-attention based deep hash (SADH) learning method is proposed, which introduces a labeled hashing center and trains the network using self-attention mechanism. The method aims to fit the image hash codes to the relevant hashing center. The distance between the hashing center and the image hash codes is calculated using a loss function, resulting in generated hash code with strong discriminative power. Experimental evaluations on standard datasets demonstrate that this method outperforms the state-of-the-art retrieval methods in terms of retrieval accuracy.
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Index Terms
- Self-Attention based Deep Hash Learning Method for Efficient Image Retrieval
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Published In
September 2023
441 pages
ISBN:9798400707667
DOI:10.1145/3627377
Copyright © 2023 ACM.
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Published: 04 December 2023
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ICBDT 2023
ICBDT 2023: 2023 6th International Conference on Big Data Technologies
September 22 - 24, 2023
Qingdao, China
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